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Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications.
Constrained Markov decision processes
Eitan Altman · 1999
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Doubly robust off-policy value evaluation for reinforcement learning
Nan Jiang and Lihong Li · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
Cited alongside, same era.
Natural policy gradient primal-dual method for constrained markov decision processes
Dongsheng Ding, Kaiqing Zhang, Tamer Basar, and Mihailo R Jovanovic · 2020
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Ipo: Interior-point policy optimization under constraints
Yongshuai Liu, Jiaxin Ding, and Xin Liu · 2020
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Responsive safety in reinforcement learning by pid lagrangian methods
Adam Stooke, Joshua Achiam, and Pieter Abbeel · 2020
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Projection-based constrained policy optimization
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge · 2020
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First order constrained optimization in policy space
Yiming Zhang, Quan Vuong, and Keith W Ross · 2020
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Constrained reinforcement learning has zero duality gap
Santiago Paternain, Luiz FO Chamon, Miguel Calvo-Fullana, and Alejandro Ribeiro · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
Cited alongside, same era.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
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YU Chao, A VELU, E VINITSKY, et al · 2021
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Provably efficient safe exploration via primal-dual policy optimization
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, and Mihailo Jovanovic · 2021
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Policy learning with constraints in model-free reinforcement learning: A survey
Yongshuai Liu, Avishai Halev, and Xin Liu · 2021
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